A real-time HPC approach for optimizing Intel multi-core architectures (Part 3 of 3)

In this three part series, Dr. Algosa Vrancic and Jeff Meisel presents findings that demonstrate how a novel approach with Intel hardware and software technology is allowing for real-time high-performance computing (HPC) in order to solve engineering problems with multi-core processors that were not possible only five years ago.

• Part 1 is a review of real-time concepts that are important for understanding this domain of engineering problems, and a comparison of traditional HPC with real-time HPC.
• Part 2 outlines software architecture approaches for utilizing multi-core processors, along with cache optimizations.
• Part 3 considers industry examples that employ this particular methodology.

Industry Examples

The following industry examples show how real-time HPC is being applied today, in many cases where only five years ago the computational results would not be achievable. All of these examples were developed with the LabVIEW programming language to take advantage of multi-core technology.

Structural Health Monitoring on China's Donghai Bridge
The Donghai Bridge, shown in Figure 17, is China's first sea-crossing bridge, stretching across the East China Sea and connecting Shanghai to Yangshan Island. The bridge has a full length of 32.50 km, a 25.32-km portion of which is above water.


Figure 17: Donghai Bridge.
Source: Wikipedia Commons


Obviously, the monitoring system for Donghai Bridge is of a large scale with a variety of quantities to be monitored and transmitted.

Modal analysis methods can be used to reflect the dynamic properties of the bridge. In fact, modal analysis is a standard engineering practice in today's structural health monitoring (SHM).

To cope with the modal analysis on large structures like bridges, however, a relatively new type of modal analysis method has been developed, which works with the data gathered at the same time the structure being analyzed is working. This is operational modal analysis. In this method, no explicit stimulus signal is applied to the structure; rather, the natural forces from the environment and the work load applied to the structure serve as the stimuli, which are random and unknown. Only the signals measured by the sensors put on the structure can be obtained and used, which serve as the response signals.

Within the operational modal analysis domain, there is a type of method that employs output-only system identification (or in other terms, time series analysis) techniques, namely, stochastic subspace identification (SSI).

In order to monitor a bridge's health status better, some informative quantities are needed to be tracked in real-time. In particular, it is highly desirable that the resonance frequencies are monitored in real-time. The challenge now is to do resonance frequency calculation online, which is a topic of current research for a wide range of applications.

To enable SSI methods to be working online, SSI needs to be reformulated to some sort of recursive fashion so as to reach the necessary computational efficiency. This is recursive stochastic subspace identification (RSSI). With RSSI, the multichannel sampled data are read and possibly decimated. The decimated data then are fed to the RSSI algorithm. Each time a new decimated data sample is fed in, a new set of resonance frequencies of the system under investigation are produced. That is, the resonance frequencies are updated as the data acquisition process goes on. If the RSSI algorithm is fast enough, this updating procedure is running in real-time.

Although further experiments need to be performed to validate the RSSI method, the results so far have shown feasibility and effectiveness of this method under the real-time requirement. With this method, the important resonance frequencies of the bridge can be tracked in real-time, which is necessary for better bridge health monitoring solutions.